• Korean Journal of Radiology
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• 제22권11호
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• pp.1764-1776
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• 2021

Objective: This study aimed to validate a deep learning-based fully automatic calcium scoring (coronary artery calcium [CAC]_auto) system using previously published cardiac computed tomography (CT) cohort data with the manually segmented coronary calcium scoring (CAC_hand) system as the reference standard. Materials and Methods: We developed the CAC_auto system using 100 co-registered, non-enhanced and contrast-enhanced CT scans. For the validation of the CAC_auto system, three previously published CT cohorts (n = 2985) were chosen to represent different clinical scenarios (i.e., 2647 asymptomatic, 220 symptomatic, 118 valve disease) and four CT models. The performance of the CAC_auto system in detecting coronary calcium was determined. The reliability of the system in measuring the Agatston score as compared with CAC_hand was also evaluated per vessel and per patient using intraclass correlation coefficients (ICCs) and Bland-Altman analysis. The agreement between CAC_auto and CAC_hand based on the cardiovascular risk stratification categories (Agatston score: 0, 1-10, 11-100, 101-400, > 400) was evaluated. Results: In 2985 patients, 6218 coronary calcium lesions were identified using CAC_hand. The per-lesion sensitivity and false-positive rate of the CAC_auto system in detecting coronary calcium were 93.3% (5800 of 6218) and 0.11 false-positive lesions per patient, respectively. The CAC_auto system, in measuring the Agatston score, yielded ICCs of 0.99 for all the vessels (left main 0.91, left anterior descending 0.99, left circumflex 0.96, right coronary 0.99). The limits of agreement between CAC_auto and CAC_hand were 1.6 ± 52.2. The linearly weighted kappa value for the Agatston score categorization was 0.94. The main causes of false-positive results were image noise (29.1%, 97/333 lesions), aortic wall calcification (25.5%, 85/333 lesions), and pericardial calcification (24.3%, 81/333 lesions). Conclusion: The atlas-based CAC_auto empowered by deep learning provided accurate calcium score measurement as compared with manual method and risk category classification, which could potentially streamline CAC imaging workflows.

• Korean Journal of Radiology
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• 제22권3호
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• pp.334-343
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• 2021

Objective: We aimed to develop a prediction model for diagnosing severe aortic stenosis (AS) using computed tomography (CT) radiomics features of aortic valve calcium (AVC) and machine learning (ML) algorithms. Materials and Methods: We retrospectively enrolled 408 patients who underwent cardiac CT between March 2010 and August 2017 and had echocardiographic examinations (240 patients with severe AS on echocardiography [the severe AS group] and 168 patients without severe AS [the non-severe AS group]). Data were divided into a training set (312 patients) and a validation set (96 patients). Using non-contrast-enhanced cardiac CT scans, AVC was segmented, and 128 radiomics features for AVC were extracted. After feature selection was performed with three ML algorithms (least absolute shrinkage and selection operator [LASSO], random forests [RFs], and eXtreme Gradient Boosting [XGBoost]), model classifiers for diagnosing severe AS on echocardiography were developed in combination with three different model classifier methods (logistic regression, RF, and XGBoost). The performance (c-index) of each radiomics prediction model was compared with predictions based on AVC volume and score. Results: The radiomics scores derived from LASSO were significantly different between the severe AS and non-severe AS groups in the validation set (median, 1.563 vs. 0.197, respectively, p < 0.001). A radiomics prediction model based on feature selection by LASSO + model classifier by XGBoost showed the highest c-index of 0.921 (95% confidence interval [CI], 0.869-0.973) in the validation set. Compared to prediction models based on AVC volume and score (c-indexes of 0.894 [95% CI, 0.815-0.948] and 0.899 [95% CI, 0.820-0.951], respectively), eight and three of the nine radiomics prediction models showed higher discrimination abilities for severe AS. However, the differences were not statistically significant (p > 0.05 for all). Conclusion: Models based on the radiomics features of AVC and ML algorithms may perform well for diagnosing severe AS, but the added value compared to AVC volume and score should be investigated further.

• 한국수학교육학회지시리즈E:수학교육논문집
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• 제32권3호
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• pp.275-296
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• 2018

본 연구에서는 수학영재 학생들을 대상으로 분석법을 이용한 증명 학습을 하게 한 후 나타나는 시선의 변화 및 시선의 변화로 야기되는 학습 성취도의 변화가 어떠한지를 알아보고자 하였다. 시선의 변화를 알아보기 위해 시선추적기법을 도입하였으며, 시선추적기를 통해 분석법의 학습효과를 좀 더 객관적으로 파악하고자 하였다. 본 연구의 결과로서, 분석법을 학습한 후 학생들이 증명 문제를 풀 때, 증명 아랫부분에서부터 증명 윗부분으로 올라가는 방식으로 시선의 이동방향이 변화하였으며 증명 아랫부분에 대한 시선 점유 비율이 윗부분에 비해 높아짐을 알 수 있었다. 또한 분석법 학습으로 야기된 시선의 변화는 증명 학습 성취도와 상관관계가 있으며 증명 학습 성취도를 향상시킨다는 것을 알 수 있었다.

• Journal of Veterinary Science
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• 제22권1호
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• pp.7.1-7.13
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• 2021

Background: Niemann-Pick disease type C (NPC) is caused by the mutation of NPC genes, which leads to the abnormal accumulation of unesterified cholesterol and glycolipids in lysosomes. This autosomal recessive disease is characterized by liver dysfunction, hepatosplenomegaly, and progressive neurodegeneration. Recently, the application of induced neural stem cells (iNSCs), converted from fibroblasts using specific transcription factors, to repair degenerated lesions has been considered a novel therapy. Objectives: The therapeutic effects on NPC by human iNSCs generated by our research group have not yet been studied in vivo; in this study, we investigate those effects. Methods: We used an NPC mouse model to efficiently evaluate the therapeutic effect of iNSCs, because neurodegeneration progress is rapid in NPC. In addition, application of human iNSCs from NPC patient-derived fibroblasts in an NPC model in vivo can give insight into the clinical usefulness of iNSC treatment. The iNSCs, generated from NPC patientderived fibroblasts using the SOX2 and HMGA2 reprogramming factors, were transplanted by intracerebral injection into NPC mice. Results: Transplantation of iNSCs showed positive results in survival and body weight change in vivo. Additionally, iNSC-treated mice showed improved learning and memory in behavior test results. Furthermore, through magnetic resonance imaging and histopathological assessments, we observed delayed neurodegeneration in NPC mouse brains. Conclusions: iNSCs converted from patient-derived fibroblasts can become another choice of treatment for neurodegenerative diseases such as NPC.

• 한국수학교육학회지시리즈E:수학교육논문집
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• 제22권3호
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• pp.275-288
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• 2008

본 연구는 수학 학습부진아들이 중학교 함수를 재학습하는 과정에서 나타나는 오류를 사례별로 분석하여 그 원인을 수학과 특성, 함수학습에서의 오류 유형 등과 결부시켜 논의를 하였다. 수학 학습부진아들은 일반적으로 학생들이 다 알고 있을 것으로 생각되는 가장 기본적인 내용에서 학습에 문제가 생길 수 있고, 주어진 문제에서 요구하는 풀이나 답이 무엇인지 정확하게 인식하지 못하여 자신에 옳다고 생각하는 부분까지만 풀거나 주어진 문제를 해석하는 데에 있어 자신이 생각하기 편리한대로 해석하여 문제를 해결하지 못하는 경우가 있다. 따라서 교사는 문제를 해결하는 과정에서 문제가 요구하는 것이 무엇인지 학생들에게 질문하고, 문제를 이해하고 계획을 세우는 단계에서 무엇을 구해야하는지 학생 스스로 알 수 있도록 돕고, 학생들이 이해할 수 있는 구체적인 상황이나 현실적인 문제에서 시작하여 그 내용을 이해시킨 후 특수한 예를 통해 일반화할 수 있도록 지도하는 방법 등 수학 학습 개선 방향을 제시할 수 있다.

• 한국축산식품학회지
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• 제40권5호
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• pp.860-861
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• 2020

• 음성과학
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• 제6권
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• pp.197-217
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• 1999

In a noisy class, the acoustic-phonetic features of the teacher and the perceptual features of learners are changed comparison with a quiet environment. Acoustical analyses were carried out on a set of French monosyllables consisting of 17 consonants and three vowel /a, e, i/, produced by 1 male speaker talking in quiet and in 50, 60 and 70 dB SPL of masking noise on headphone. The results of the acoustic analyses showed consistent differences in energy and formant center frequency amplitude of consonants and vowels, $F_1$ frequency of vowel and duration of voiceless stops suggesting the increase of vocal effort. The perceptual experiments in which 18 undergraduate female students learning French served as the subjects, were conducted in quiet and in 50, 60 dB of masking noise. The identification scores on consonants were higher in Lombard speech than in normal speech, suggesting that the speaker's vocal effort is useful to overcome the masking effect of noise. And, with increased noise level, the perceptual response to the French consonants given had a tendency to be complex and the subjective reaction score on the noise using the vocabulary representative of 'unpleasant' sensation to be higher. And, in the point of view on the L2(second language) acquisition, the influence of L1 (first language) on L2 examined in the perceptual result supports the interference theory.

• 대한물리의학회지
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• 제13권2호
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• pp.97-107
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• 2018

PURPOSE: This study investigated the job-centered curriculum in the Department of Physiotherapy at the University of Brighton, England, to examine NCS (national competency standards) curriculum in physiotherapy. METHODS: The researcher visited the University of Brighton from September 2015 to May 2016 and conducted interviews with faculty members. Data were collected through the university's website and the Chartered Society of Physiotherapy. RESULTS: The undergraduate program is a three-year program and with a module system. There is a course leader for each module. Each grade requires 120 credits (10 hours per credit) and credit hours can be earned through lectures, tutorials, practical sessions, self-directed e-learning, group work, inter-professional classes, and seminars. Clinical placement is carried out six times during 3 years, for a total of 32 weeks, 35 hours per week, 1120 hours in total. Students are enrolled as a student members of the Chartered Society of Physiotherapy and are covered by professional liability insurance during clinical placement. The Center for Teaching and Learning holds regular workshops to discuss curriculum and module design, conduct course reviews, and review student assessment and feedback. All courses at the university must be approved, monitored annually, and re-approved every 5 years. CONCLUSION: This study can contribute to the development and operation of the NCS physiotherapy curriculum, as well as to the development of modules and assessment tools related to the application of this curriculum.

• 한국수학교육학회지시리즈E:수학교육논문집
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• 제23권1호
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• pp.129-144
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• 2009

본 연구에서는 초등학교 3학년 학생들을 대상으로 Lesh 표상 변환 모델을 적용한 RNP 교재의 사용이 분수에 대한 아동의 개념 이해와 문제 해결력에 어떤 영향을 미치는지를 알아보았다. RNP 교재의 사용은 아동들의 분수에 대한 개념적 이해를 향상시켰을 뿐 아니라 그들의 문제해결 능력 또한 향상시켰다. RNP 교재가 제공하는 다양한 구체적 조작 활동 및 표상 변환 활동을 통해서 아동들은 등분할로서의 분수의 개념에 대한 이해를 더욱 명확히 하였고, 개념적 이해를 토대로 다양한 문제 상황에서 적절한 문제 해결 전략을 사용하여 문제를 해결하였다. 특히, 후속 학습 내용인 분수의 크기 비교에 관한 문제 상황에서 아동들은 선행 학습 과정에서 만들어진 심상이나 수학적 경험을 토대로 올바른 추론 과정을 보여주었다.

• 대한안전경영과학회지
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• 제22권1호
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• pp.9-14
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• 2020

Artificial intelligence (AI) has been applied to most industries by enhancing automation and contributing greatly to efficient processes and high-quality production. This research analyzes the applications of AI-based automobile accident prevention systems. It deals with AI-based collision prevention systems that learn information from various sensors attached to cars and AI-based accident detection systems that automatically report accidents to the control center in the event of a collision. Based on the literature review, technological and institutional changes are taking place at the national levels, which recognize the effectiveness of the systems. In addition, start-ups at home and abroad as well as major car manufacturers are in the process of commercializing auto parts equipped with AI-based collision prevention technology.